Multiple regression genetic programming

Arnaldo, Ignacio; Krawiec, Krzysztof; O’Reilly, Una-May

doi:10.1145/2576768.2598291

Cited by 103 publications

(72 citation statements)

References 17 publications

(11 reference statements)

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“…Recently, several methods emerged [1], [2], [15], [21], [22] that explicitly restrict the class of models to generalized linear models, i.e. to a linear combination of possibly non-linear basis functions.…”

Section: Hybrid Sngp With Linear Regressionmentioning

confidence: 99%

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Hybrid Single Node Genetic Programming for Symbolic Regression

Kubalı́k

Alibekov

Žegklitz

et al. 2016

Transactions on Computational Collective Intelligence XXIV

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Abstract. This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact version of the best-performing graph to the beginning and to the end of the population, respectively, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on five symbolic regression benchmarks and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to improve the performance of the SNGP algorithm. We then propose two variants of hybrid SNGP utilizing a linear regression technique, LASSO, to improve its performance. The proposed algorithms have been compared to the state-of-the-art symbolic regression methods that also make use of the linear regression techniques on four real-world benchmarks. The results show the hybrid SNGP algorithms are at least competitive with or better than the compared methods.

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Section: Hybrid Sngp With Linear Regressionmentioning

confidence: 99%

“…This results in a very fast algorithm. The original paper reports EFS being comparable to neural networks and similar or better than Multiple Regression Genetic Programming [1] which itself was reported to outperform conventional GP, multiple regression and Scaled Symbolic Regression [11].…”

Section: Hybrid Sngp With Linear Regressionmentioning

confidence: 99%

Hybrid Single Node Genetic Programming for Symbolic Regression

Kubalı́k

Alibekov

Žegklitz

et al. 2016

Transactions on Computational Collective Intelligence XXIV

View full text Add to dashboard Cite

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“…A more recent method, named Multiple Regression GP (MRGP) [12], also assigns coefficients to the elements of the resulting function, which are optimized by regression. The method consists in a GP with selection strategy based on Nondominated Sorting Genetic Algorithm II (NSGA-II) [13] with two minimization objectives, the model subtree complexity measure and the multiple regression error.…”

Section: Related Workmentioning

confidence: 99%

Revisiting the Sequential Symbolic Regression Genetic Programming

Oliveira

Otero

Miranda

et al. 2016

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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Abstract-Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compared with canonical Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP). In this paper we revisit SSR exploring the method behaviour in higher dimensional, larger and more heterogeneous datasets. We discuss the difficulties arising from the application of the method to more complex problems, e.g., overfitting, along with suggestions to overcome them. An experimental analysis was conducted comparing SSR to GP and GSGP, showing SSR solutions are smaller than those generated by the GSGP with similar performance and more accurate than those generated by the canonical GP.

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“…In [2], the authors propose to linearly combine subexpressions of programs to re-interpret their semantics. In this work, however, we propose to apply a linear combination of two distinct programs.…”

Section: Optimized Non-convex Geometric Semantic Crossover Operatormentioning

confidence: 99%

The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

Albinati

Pappa

Otero

et al. 2015

Lecture Notes in Computer Science

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Abstract. This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.

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Multiple regression genetic programming

Cited by 103 publications

References 17 publications

Hybrid Single Node Genetic Programming for Symbolic Regression

Hybrid Single Node Genetic Programming for Symbolic Regression

Revisiting the Sequential Symbolic Regression Genetic Programming

The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

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